{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee9823e2-13d1-42bb-aac9-9a88258bfd35",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install langchain langchain-huggingface huggingface-hub  duckduckgo-search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a36b4344-b4a8-477d-9036-d778000f5300",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install html2text faiss-cpu streamlit chromadb langchain-community"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "949fc19a-8d6f-47c9-a546-4f2b2c6f8b0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install numexpr youtube_search wikipedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "eb45b048-a316-475e-98cf-0de0ee92b9c8",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0be29999240743d6b827b43995a58935",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>billion_parameters</th>\n",
       "      <th>tokens_per_second</th>\n",
       "      <th>tokens_per_second_per_billion_parameters</th>\n",
       "      <th>answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mistral-7B-Instruct-v0.1</td>\n",
       "      <td>7</td>\n",
       "      <td>3.553594</td>\n",
       "      <td>0.507656</td>\n",
       "      <td>describe briefly what is an AI agent\\n\\nAn AI ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 model_name  billion_parameters  tokens_per_second  \\\n",
       "0  Mistral-7B-Instruct-v0.1                   7           3.553594   \n",
       "\n",
       "   tokens_per_second_per_billion_parameters  \\\n",
       "0                                  0.507656   \n",
       "\n",
       "                                              answer  \n",
       "0  describe briefly what is an AI agent\\n\\nAn AI ...  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import time\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "\n",
    "def measure_model_performance(model_name, max_length, input_text,\n",
    "                             model_size_billion_params):\n",
    "    # Load tokenizer and model\n",
    "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "    model = AutoModelForCausalLM.from_pretrained(model_name)\n",
    "    \n",
    "    # Tokenize the input text\n",
    "    input_tokens = tokenizer(input_text, return_tensors=\"pt\")\n",
    "    \n",
    "    # Measure the time taken to generate the output\n",
    "    start_time = time.time()\n",
    "    output = model.generate(**input_tokens, max_length=max_length)\n",
    "    end_time = time.time()\n",
    "    \n",
    "    # Decode the generated tokens to text\n",
    "    output_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
    "    \n",
    "    # Calculate the time taken for generation\n",
    "    time_taken = end_time - start_time\n",
    "    \n",
    "    # Calculate the number of tokens generated\n",
    "    num_tokens = output.size(1)\n",
    "    \n",
    "    # Calculate tokens per second\n",
    "    tokens_per_second = num_tokens / time_taken\n",
    "    \n",
    "    # Calculate tokens per second per billion parameters\n",
    "    tokens_per_second_per_billion_params = tokens_per_second / model_size_billion_params\n",
    "    \n",
    "    # Create the data list\n",
    "    data = {\n",
    "        \"model_name\": model_name.split('/')[1],\n",
    "        \"billion_parameters\": model_size_billion_params,\n",
    "        \"tokens_per_second\": tokens_per_second,\n",
    "        \"tokens_per_second_per_billion_parameters\": tokens_per_second_per_billion_params,\n",
    "        \"answer\": output_text\n",
    "    }\n",
    "    \n",
    "    return data\n",
    "\n",
    "# Example usage\n",
    "model_name = \"mistralai/Mistral-7B-Instruct-v0.1\"\n",
    "max_length = 512\n",
    "input_text = \"describe briefly what is an AI agent\"\n",
    "model_size_billion_params = 7\n",
    "\n",
    "data = measure_model_performance(model_name, max_length, \n",
    "                                 input_text, model_size_billion_params)\n",
    "\n",
    "# Convert data to DataFrame and display\n",
    "df = pd.DataFrame([data])\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9f94983f-a947-45b3-96e8-9192df613e69",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cbe999144ece400998c5b758cc67ed25",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3927c576115847b3828b277a697ed35f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "You are not running the flash-attention implementation, expect numerical differences.\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1342f680163f40eebd9d3ffdecbb4fa9",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n",
      "The fast path is not available because on of `(selective_state_update, selective_scan_fn, causal_conv1d_fn, causal_conv1d_update, mamba_inner_fn)` is None. Falling back to the naive implementation. To install follow https://github.com/state-spaces/mamba/#installation and https://github.com/Dao-AILab/causal-conv1d\n",
      "Some weights of the model checkpoint at tri-ml/mamba-7b-rw were not used when initializing MambaForCausalLM: ['model.lm_head.weight']\n",
      "- This IS expected if you are initializing MambaForCausalLM from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing MambaForCausalLM from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d244ef2b122b4a3ca949cd1d5763c3a4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>billion_parameters</th>\n",
       "      <th>tokens_per_second</th>\n",
       "      <th>tokens_per_second_per_billion_parameters</th>\n",
       "      <th>answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mistral-7B-Instruct-v0.1</td>\n",
       "      <td>7</td>\n",
       "      <td>3.773306</td>\n",
       "      <td>0.539044</td>\n",
       "      <td>[INST]describe briefly what is an AI agent[/IN...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Phi-3-mini-4k-instruct</td>\n",
       "      <td>3</td>\n",
       "      <td>6.158225</td>\n",
       "      <td>2.052742</td>\n",
       "      <td>describe briefly what is an AI agent An AI age...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Qwen2-7B-Instruct</td>\n",
       "      <td>7</td>\n",
       "      <td>3.416965</td>\n",
       "      <td>0.488138</td>\n",
       "      <td>describe briefly what is an AI agent\\n\\nAn AI ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mamba-7b-rw</td>\n",
       "      <td>7</td>\n",
       "      <td>2.909034</td>\n",
       "      <td>0.415576</td>\n",
       "      <td>describe briefly what is an AI agent, and what...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Meta-Llama-3-8B-Instruct</td>\n",
       "      <td>8</td>\n",
       "      <td>3.070035</td>\n",
       "      <td>0.383754</td>\n",
       "      <td>describe briefly what is an AI agent\\nAn AI ag...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 model_name  billion_parameters  tokens_per_second  \\\n",
       "0  Mistral-7B-Instruct-v0.1                   7           3.773306   \n",
       "1    Phi-3-mini-4k-instruct                   3           6.158225   \n",
       "2         Qwen2-7B-Instruct                   7           3.416965   \n",
       "3               mamba-7b-rw                   7           2.909034   \n",
       "4  Meta-Llama-3-8B-Instruct                   8           3.070035   \n",
       "\n",
       "   tokens_per_second_per_billion_parameters  \\\n",
       "0                                  0.539044   \n",
       "1                                  2.052742   \n",
       "2                                  0.488138   \n",
       "3                                  0.415576   \n",
       "4                                  0.383754   \n",
       "\n",
       "                                              answer  \n",
       "0  [INST]describe briefly what is an AI agent[/IN...  \n",
       "1  describe briefly what is an AI agent An AI age...  \n",
       "2  describe briefly what is an AI agent\\n\\nAn AI ...  \n",
       "3  describe briefly what is an AI agent, and what...  \n",
       "4  describe briefly what is an AI agent\\nAn AI ag...  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame()\n",
    "\n",
    "# List of models to evaluate\n",
    "models = [\n",
    "    \"mistralai/Mistral-7B-Instruct-v0.1\",\n",
    "    \"microsoft/Phi-3-mini-4k-instruct\",\n",
    "    \"Qwen/Qwen2-7B-Instruct\",\n",
    "    \"tri-ml/mamba-7b-rw\",\n",
    "    \"meta-llama/Meta-Llama-3-8B-Instruct\"\n",
    "]\n",
    "\n",
    "# List of input texts to use with each model\n",
    "input_texts = [\n",
    "    \"[INST]describe briefly what is an AI agent[/INST]\",\n",
    "    \"<|user|>describe briefly what is an AI agent<|end|><|assistant|>\",\n",
    "    \"describe briefly what is an AI agent\",\n",
    "    \"describe briefly what is an AI agent\",\n",
    "    \"describe briefly what is an AI agent\"\n",
    "]\n",
    "\n",
    "parameters = [7, 3, 7, 7, 8]\n",
    "\n",
    "# Ensure both lists have the same length\n",
    "assert len(models) == len(input_texts), \"The number of models must match the number of input texts.\"\n",
    "\n",
    "results = []\n",
    "# Measure performance for each model and append to the DataFrame\n",
    "for model_name, input_text, model_size_billion_params in zip(models, input_texts, parameters):\n",
    "    data = measure_model_performance(model_name, max_length, input_text, \n",
    "                                     model_size_billion_params)\n",
    "    results.append(data)\n",
    "\n",
    "df = pd.DataFrame(results)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8b2aeb2-f784-4bcd-bcca-eb288134bdc7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "# Plotting the bar plot for tokens per second\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(df['model_name'], df['tokens_per_second'], color='skyblue')\n",
    "plt.xlabel('Model Name')\n",
    "plt.ylabel('Tokens per second')\n",
    "plt.title('Tokens per second by model')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.savefig('Token_second.jpg', format='jpeg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "59ddf5ef-0bfd-4ae1-95f2-5e967ca5f561",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.bar(df['model_name'], df['tokens_per_second_per_billion_parameters'], color='skyblue')\n",
    "plt.xlabel('Model Name')\n",
    "plt.ylabel('Tokens per second \\n for billion parameters')\n",
    "plt.title('Tokens per second for billion parameters')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()\n",
    "plt.savefig('Token_second_B_param.jpg', format='jpeg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "91b5e8d9-395b-4c99-8a8c-04330d2e0933",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can print the answer:\n",
    "for i in range(5):\n",
    "    print(df.loc[i,'model_name'])\n",
    "    print(df.loc[i,'answer'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aa9a399f-b61a-48eb-8fbf-0daab94517ac",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = {\n",
    "    \"Model\": [\n",
    "        \"Mistral-7B-Instruct-v0.1\",\n",
    "        \"Phi-3-mini-4k-instruct\",\n",
    "        \"Qwen2-7B-Instruct\",\n",
    "        \"mamba-7b-rw\",\n",
    "        \"Meta-Llama-3-8B-Instruct\"\n",
    "    ],\n",
    "    \"Overall Quality\": [8, 9, 10, 1, 8],\n",
    "    \"Completeness\": [8, 9, 10, 1, 8],\n",
    "    \"Truthfulness\": [9, 9, 10, 1, 9]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Plotting the data\n",
    "df.set_index(\"Model\").plot(kind=\"bar\", figsize=(12, 6), color=['#1f77b4', '#ff7f0e', '#2ca02c'])\n",
    "plt.title('Scores for AI models')\n",
    "plt.xlabel('Models')\n",
    "plt.ylabel('Scores')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.legend(title='Score Type')\n",
    "plt.tight_layout()\n",
    "plt.savefig('evaluation.jpg', format='jpeg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4345475d-e2db-4333-81f8-68bd59a72b98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Sergio Mattarella OMRI OMCA (Italian pronunciation: [ˈsɛrdʒo mattaˈrɛlla]; born 23 July 1941) is an Italian politician, statesman, jurist, academic, and lawyer who is currently serving as the 12th president of Italy since 2015. He is the longest-serving president in the history of the Italian Republic.Since Giorgio Napolitano's death in 2023, Mattarella has been the only living Italian ... The 2022 Italian presidential election was held in Rome between 24 and 29 January 2022. The president of Italy was elected by a joint assembly composed of the Italian Parliament and regional representatives. The election process extended over multiple days, culminating in incumbent president Sergio Mattarella being confirmed for a second term, with a total of 759 votes on the eighth ballot. European Commission President Ursula von der Leyen, Canadian Prime Minister Justin Trudeau, US President Joe Biden, UK Prime Minister Rishi Sunak, French President Emmanuel Macron and German ... ROME, June 11 (Reuters) - Italy hosts the annual summit of leaders from the Group of Seven (G7) major democracies on June 13-15. Here are key facts and figures about the three-d ay event. UNITED NATIONS (AP) — Italy's president told the U.N. General Assembly on Tuesday that Russia's invasion of Ukraine can't be solved by rewarding its aggression and peace can only come when Ukraine's sovereignty and territorial integrity are restored.. Sergio Mattarella said Italy, which now heads the G7 meetings, and many international partners have come to Ukraine's defense to ...\""
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain.tools import DuckDuckGoSearchRun\n",
    "\n",
    "ddg_search = DuckDuckGoSearchRun()\n",
    "ddg_search.run('Who is the current president of Italy?')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ee692e9d-adcf-4eb9-a589-b7f4a61a161b",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.agents import Tool\n",
    "\n",
    "tools = [\n",
    "   Tool(\n",
    "       name=\"DuckDuckGo Search\",\n",
    "       func=ddg_search.run,\n",
    "       description=\"A web search tool to extract information from Internet.\",\n",
    "   )\n",
    "]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1cc296db-6397-4b52-96c7-550b262e66dd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#!pip install google-serp-api\n",
    "import os\n",
    "SERPER_API_KEY = 'your_key'\n",
    "os.environ[\"SERPER_API_KEY\"] = SERPER_API_KEY\n",
    "\n",
    "from langchain.utilities import GoogleSerperAPIWrapper\n",
    "\n",
    "google_search = GoogleSerperAPIWrapper()\n",
    "\n",
    "tools.append(\n",
    "   Tool(\n",
    "       name=\"Google Web Search\",\n",
    "       func=google_search.run,\n",
    "       description=\"Google search tool to extract information from Internet.\",\n",
    "   )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9171d678-77b7-4686-a1f0-8c514eb80e4d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.tools import WikipediaQueryRun\n",
    "from langchain.utilities import WikipediaAPIWrapper\n",
    "\n",
    "wikipedia = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper())\n",
    "\n",
    "tools.append(\n",
    "   Tool(\n",
    "       name=\"Wikipedia Web Search\",\n",
    "       func=wikipedia.run,\n",
    "       description=\"Useful tool to search Wikipedia.\",\n",
    "   )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "347a6161-b611-4422-8459-8c76ae0ac138",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
    "from langchain_huggingface import HuggingFacePipeline\n",
    "from langchain.agents import load_tools\n",
    "from langchain.agents import initialize_agent\n",
    "model_id = \"microsoft/Phi-3-mini-4k-instruct\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_id,\n",
    "    load_in_4bit=True,\n",
    "    #attn_implementation=\"flash_attention_2\", # if you have an ampere GPU\n",
    ")\n",
    "\n",
    "# Define the text generation pipeline using HuggingFace transformers\n",
    "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer, max_new_tokens=500, top_k=50, temperature=0.1)\n",
    "\n",
    "# Wrap the pipeline in a HuggingFacePipeline object\n",
    "llm = HuggingFacePipeline(pipeline=pipe)\n",
    "\n",
    "\n",
    "agent = initialize_agent(\n",
    "   tools, llm, agent=\"zero-shot-react-description\", verbose=True,\n",
    "    handle_parsing_errors=True,\n",
    "    max_iterations=1,\n",
    ")\n",
    "\n",
    "# Define the query\n",
    "query = \"Who is the current president of Italy? Who was the previous one?\"\n",
    "\n",
    "# Execute the agent query\n",
    "response = agent.run(query)\n",
    "print(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5bd6eb2-ac3d-471e-a2b2-dde06996b253",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
    "from langchain_huggingface import HuggingFacePipeline\n",
    "from langchain.agents import load_tools, AgentExecutor, initialize_agent\n",
    "from langchain_core.prompts import PromptTemplate\n",
    "\n",
    "# Load the model and tokenizer\n",
    "model_id = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_id,\n",
    "    load_in_4bit=True,\n",
    ")\n",
    "\n",
    "# Define the text generation pipeline using HuggingFace transformers\n",
    "pipe = pipeline(\"text-generation\", model=model, \n",
    "                tokenizer=tokenizer, max_new_tokens=500, \n",
    "                top_k=50, temperature=0.1, \n",
    "               do_sample=True)\n",
    "\n",
    "# Wrap the pipeline in a HuggingFacePipeline object\n",
    "llm = HuggingFacePipeline(pipeline=pipe)\n",
    "\n",
    "# Load the necessary tools\n",
    "tools = load_tools([\"ddg-search\",  \"llm-math\", \"wikipedia\"], llm=llm)\n",
    "\n",
    "# Define the prompt template with explicit stop instructions\n",
    "template = '''Answer the following question as best as you can. You have access to the following tools:\n",
    "\n",
    "{tools}\n",
    "\n",
    "Use the following format:\n",
    "\n",
    "Question: {input}\n",
    "Thought: You should think about what action to take\n",
    "Action: the action to take, should be one of [{tool_names}]\n",
    "Action Input: the input to the action\n",
    "Observation: the result of the action\n",
    "Thought: I now know the final answer\n",
    "Final Answer: the final answer to the original input question\n",
    "\n",
    "Do not answer or ask any other questions. Stop once you have provided the Final Answer.\n",
    "\n",
    "\n",
    "Begin!\n",
    "\n",
    "Question: {input}\n",
    "'''\n",
    "\n",
    "# Create a PromptTemplate from the template\n",
    "prompt = PromptTemplate.from_template(template)\n",
    "\n",
    "# Initialize the agent using initialize_agent\n",
    "agent = initialize_agent(\n",
    "    tools=tools,\n",
    "    llm=llm,\n",
    "    agent=\"zero-shot-react-description\",\n",
    "    prompt=prompt,\n",
    "    verbose=True,\n",
    "    handle_parsing_errors=True,\n",
    "    max_iterations=1,\n",
    "    stop_sequence=\"Final Answer:\" \n",
    ")\n",
    "\n",
    "# Define the query\n",
    "query = \"The biography of Napoleon\"\n",
    "\n",
    "# Execute the query\n",
    "response = agent.run(query)\n",
    "print(response)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99a94de2-8241-4eed-8823-8c17fcb8c93a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline\n",
    "from langchain_huggingface import HuggingFacePipeline\n",
    "from langchain.agents import load_tools\n",
    "from langchain.agents import initialize_agent\n",
    "model_id = \"mistralai/Mistral-7B-Instruct-v0.1\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    model_id,\n",
    "    load_in_4bit=True,\n",
    "    #attn_implementation=\"flash_attention_2\", # if you have an ampere GPU\n",
    ")\n"
   ]
  }
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